A novel white blood cell segmentation scheme based on feature space clustering
Abstract This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. Fi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jiang, Kan [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2005 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag 2005 |
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| Übergeordnetes Werk: |
Enthalten in: Soft computing - Springer-Verlag, 1997, 10(2005), 1 vom: 08. Apr., Seite 12-19 |
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| Übergeordnetes Werk: |
volume:10 ; year:2005 ; number:1 ; day:08 ; month:04 ; pages:12-19 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00500-005-0458-z |
|---|
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OLC2034863992 |
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| 520 | |a Abstract This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods. | ||
| 650 | 4 | |a WBC segmentation | |
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10.1007/s00500-005-0458-z doi (DE-627)OLC2034863992 (DE-He213)s00500-005-0458-z-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Jiang, Kan verfasserin aut A novel white blood cell segmentation scheme based on feature space clustering 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2005 Abstract This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods. WBC segmentation Feature space clustering Scale-space filtering Watershed Liao, Qing-Min aut Xiong, Yuan aut Enthalten in Soft computing Springer-Verlag, 1997 10(2005), 1 vom: 08. Apr., Seite 12-19 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:10 year:2005 number:1 day:08 month:04 pages:12-19 https://doi.org/10.1007/s00500-005-0458-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_40 GBV_ILN_70 GBV_ILN_95 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 10 2005 1 08 04 12-19 |
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10.1007/s00500-005-0458-z doi (DE-627)OLC2034863992 (DE-He213)s00500-005-0458-z-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Jiang, Kan verfasserin aut A novel white blood cell segmentation scheme based on feature space clustering 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2005 Abstract This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods. WBC segmentation Feature space clustering Scale-space filtering Watershed Liao, Qing-Min aut Xiong, Yuan aut Enthalten in Soft computing Springer-Verlag, 1997 10(2005), 1 vom: 08. Apr., Seite 12-19 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:10 year:2005 number:1 day:08 month:04 pages:12-19 https://doi.org/10.1007/s00500-005-0458-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_40 GBV_ILN_70 GBV_ILN_95 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 10 2005 1 08 04 12-19 |
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10.1007/s00500-005-0458-z doi (DE-627)OLC2034863992 (DE-He213)s00500-005-0458-z-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Jiang, Kan verfasserin aut A novel white blood cell segmentation scheme based on feature space clustering 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2005 Abstract This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods. WBC segmentation Feature space clustering Scale-space filtering Watershed Liao, Qing-Min aut Xiong, Yuan aut Enthalten in Soft computing Springer-Verlag, 1997 10(2005), 1 vom: 08. Apr., Seite 12-19 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:10 year:2005 number:1 day:08 month:04 pages:12-19 https://doi.org/10.1007/s00500-005-0458-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_40 GBV_ILN_70 GBV_ILN_95 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 10 2005 1 08 04 12-19 |
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10.1007/s00500-005-0458-z doi (DE-627)OLC2034863992 (DE-He213)s00500-005-0458-z-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Jiang, Kan verfasserin aut A novel white blood cell segmentation scheme based on feature space clustering 2005 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2005 Abstract This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods. WBC segmentation Feature space clustering Scale-space filtering Watershed Liao, Qing-Min aut Xiong, Yuan aut Enthalten in Soft computing Springer-Verlag, 1997 10(2005), 1 vom: 08. Apr., Seite 12-19 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:10 year:2005 number:1 day:08 month:04 pages:12-19 https://doi.org/10.1007/s00500-005-0458-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_40 GBV_ILN_70 GBV_ILN_95 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 10 2005 1 08 04 12-19 |
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Abstract This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods. © Springer-Verlag 2005 |
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Abstract This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods. © Springer-Verlag 2005 |
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Abstract This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods. © Springer-Verlag 2005 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2034863992</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502111447.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2005 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-005-0458-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2034863992</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00500-005-0458-z-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">11</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Jiang, Kan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A novel white blood cell segmentation scheme based on feature space clustering</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2005</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag 2005</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. 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